Overview

Dataset statistics

Number of variables11
Number of observations508
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.8 KiB
Average record size in memory88.3 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:58:32.661598
Analysis finished2020-08-25 00:58:51.110651
Duration18.45 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Cardiovascular_Mortality is highly correlated with Total_MortalityHigh correlation
Total_Mortality is highly correlated with Cardiovascular_MortalityHigh correlation

Variables

Total_Mortality
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count483
Unique (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.04816923366758
Minimum142.1300048828125
Maximum231.72999572753903
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:51.152618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum142.1300049
5-th percentile150.2910034
Q1159.6350021
median166.7400055
Q3176.4124947
95-th percentile196.4249977
Maximum231.7299957
Range89.59999084
Interquartile range (IQR)16.77749252

Descriptive statistics

Standard deviation14.18287311
Coefficient of variation (CV)0.08389841294
Kurtosis1.782809551
Mean169.0481692
Median Absolute Deviation (MAD)8.34500885
Skewness1.05095701
Sum85876.46997
Variance201.1538896
2020-08-25T00:58:51.259420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
151.919998230.6%
 
165.240005520.4%
 
182.279998820.4%
 
184.669998220.4%
 
170.270004320.4%
 
158.660003720.4%
 
163.029998820.4%
 
154.419998220.4%
 
167.550003120.4%
 
182.369995120.4%
 
166.520004320.4%
 
160.429992720.4%
 
161.639999420.4%
 
185.619995120.4%
 
163.160003720.4%
 
157.759994520.4%
 
165.419998220.4%
 
161.410003720.4%
 
180.779998820.4%
 
160.809997620.4%
 
171.270004320.4%
 
16620.4%
 
165.979995720.4%
 
206.970001220.4%
 
157.899993910.2%
 
Other values (458)45890.2%
 
ValueCountFrequency (%) 
142.130004910.2%
 
142.410003710.2%
 
143.539993310.2%
 
144.070007310.2%
 
144.7510.2%
 
145.100006110.2%
 
145.300003110.2%
 
145.320007310.2%
 
145.330001810.2%
 
146.2510.2%
 
ValueCountFrequency (%) 
231.729995710.2%
 
229.320007310.2%
 
222.559997610.2%
 
216.669998210.2%
 
213.899993910.2%
 
212.960006710.2%
 
212.240005510.2%
 
208.259994510.2%
 
206.970001220.4%
 
206.259994510.2%
 

Cardiovascular_Mortality
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count478
Unique (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.69887787526048
Minimum68.11000061035156
Maximum132.03999328613278
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:51.370057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum68.11000061
5-th percentile74.58000183
Q181.90000343
median87.33000183
Q394.36000061
95-th percentile106.9120007
Maximum132.0399933
Range63.92999268
Interquartile range (IQR)12.45999718

Descriptive statistics

Standard deviation9.998688157
Coefficient of variation (CV)0.112726208
Kurtosis1.018111914
Mean88.69887788
Median Absolute Deviation (MAD)6.205001831
Skewness0.8092612807
Sum45059.02996
Variance99.97376486
2020-08-25T00:58:51.475085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
87.3300018330.6%
 
97.4000015330.6%
 
74.5800018320.4%
 
83.3000030520.4%
 
87.0199966420.4%
 
87.0299987820.4%
 
95.2900009220.4%
 
86.8300018320.4%
 
89.3499984720.4%
 
92.8399963420.4%
 
80.5299987820.4%
 
82.9100036620.4%
 
90.4800033620.4%
 
81.5299987820.4%
 
92.3399963420.4%
 
81.1699981720.4%
 
94.3600006120.4%
 
85.3300018320.4%
 
85.5100021420.4%
 
88.6100006120.4%
 
85.5899963420.4%
 
94.1900024420.4%
 
73.6299972520.4%
 
83.9199981720.4%
 
82.4700012220.4%
 
Other values (453)45689.8%
 
ValueCountFrequency (%) 
68.1100006110.2%
 
68.4599990810.2%
 
70.9599990810.2%
 
71.0199966410.2%
 
71.510.2%
 
71.9599990810.2%
 
72.0500030510.2%
 
72.3799972510.2%
 
72.7510.2%
 
72.8399963410.2%
 
ValueCountFrequency (%) 
132.039993310.2%
 
126.949996910.2%
 
121.110000610.2%
 
119.300003110.2%
 
118.589996310.2%
 
118.199996910.2%
 
116.040000910.2%
 
115.989997910.2%
 
115.980003410.2%
 
114.410003710.2%
 

Temperature
Real number (ℝ≥0)

Distinct count473
Unique (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.26041332755501
Minimum50.90999984741211
Maximum99.87999725341795
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:51.594443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum50.90999985
5-th percentile60.23899975
Q167.23500061
median74.05500031
Q381.48999786
95-th percentile88.19949951
Maximum99.87999725
Range48.96999741
Interquartile range (IQR)14.25499725

Descriptive statistics

Standard deviation9.01357783
Coefficient of variation (CV)0.1213779647
Kurtosis-0.4415462271
Mean74.26041333
Median Absolute Deviation (MAD)7.055000305
Skewness0.09515390921
Sum37724.28997
Variance81.24458529
2020-08-25T00:58:51.849334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
78.6100006130.6%
 
68.3499984730.6%
 
63.9500007630.6%
 
80.2300033620.4%
 
76.9899978620.4%
 
73.0699996920.4%
 
63.9099998520.4%
 
82.0800018320.4%
 
69.5699996920.4%
 
61.9099998520.4%
 
86.7900009220.4%
 
75.6800003120.4%
 
76.3899993920.4%
 
62.9399986320.4%
 
69.6299972520.4%
 
78.4899978620.4%
 
86.1399993920.4%
 
77.9899978620.4%
 
66.0999984720.4%
 
71.8899993920.4%
 
64.8499984720.4%
 
81.4899978620.4%
 
69.0899963420.4%
 
66.7520.4%
 
64.2799987820.4%
 
Other values (448)45589.6%
 
ValueCountFrequency (%) 
50.9099998510.2%
 
51.2200012210.2%
 
51.2599983210.2%
 
54.8400001510.2%
 
54.8499984710.2%
 
55.1800003110.2%
 
55.3300018310.2%
 
56.3199996910.2%
 
56.5200004610.2%
 
56.6800003110.2%
 
ValueCountFrequency (%) 
99.8799972510.2%
 
98.9499969510.2%
 
97.1999969510.2%
 
97.1500015310.2%
 
97.0500030510.2%
 
96.2300033610.2%
 
95.3399963410.2%
 
93.4300003110.2%
 
92.5899963410.2%
 
92.5599975610.2%
 

Relative_Humidity
Real number (ℝ≥0)

Distinct count478
Unique (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.83080704756609
Minimum17.649999618530273
Maximum93.01000213623048
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:51.955412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum17.64999962
5-th percentile36.09799957
Q151.83750057
median60.45999908
Q367.125
95-th percentile74.93249855
Maximum93.01000214
Range75.36000252
Interquartile range (IQR)15.28749943

Descriptive statistics

Standard deviation11.84406348
Coefficient of variation (CV)0.2013241714
Kurtosis0.2911220793
Mean58.83080705
Median Absolute Deviation (MAD)7.559997559
Skewness-0.5695628496
Sum29886.04998
Variance140.2818398
2020-08-25T00:58:52.065013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
57.9700012230.6%
 
70.0299987820.4%
 
70.8300018320.4%
 
63.6800003120.4%
 
46.8699989320.4%
 
55.2799987820.4%
 
69.6999969520.4%
 
61.6800003120.4%
 
52.1899986320.4%
 
65.7300033620.4%
 
65.1900024420.4%
 
60.7700004620.4%
 
65.5500030520.4%
 
59.0699996920.4%
 
75.3600006120.4%
 
65.6699981720.4%
 
55.5499992420.4%
 
52.7999992420.4%
 
55.6100006120.4%
 
65.8499984720.4%
 
56.6899986320.4%
 
62.2799987820.4%
 
68.3399963420.4%
 
66.3899993920.4%
 
69.5800018320.4%
 
Other values (453)45790.0%
 
ValueCountFrequency (%) 
17.6499996210.2%
 
23.1399993910.2%
 
26.2700004610.2%
 
27.510.2%
 
27.8199996910.2%
 
28.3700008410.2%
 
28.510.2%
 
28.6900005310.2%
 
29.2000007610.2%
 
29.8600006110.2%
 
ValueCountFrequency (%) 
93.0100021410.2%
 
88.3099975610.2%
 
86.9499969510.2%
 
85.5699996910.2%
 
85.3399963410.2%
 
83.8600006110.2%
 
81.8899993910.2%
 
80.3700027510.2%
 
77.6299972510.2%
 
77.3099975610.2%
 

Carbon_Monoxide
Real number (ℝ≥0)

Distinct count409
Unique (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.909291334039583
Minimum2.5199999809265137
Maximum22.38999938964844
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:52.177742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.519999981
5-th percentile3.660500085
Q14.96999979
median6.86500001
Q310.07999969
95-th percentile14.6665
Maximum22.38999939
Range19.86999941
Interquartile range (IQR)5.109999895

Descriptive statistics

Standard deviation3.762013102
Coefficient of variation (CV)0.4756447757
Kurtosis1.161940102
Mean7.909291334
Median Absolute Deviation (MAD)2.170000076
Skewness1.147543664
Sum4017.919998
Variance14.15274258
2020-08-25T00:58:52.284509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5.13999986640.8%
 
5.40999984730.6%
 
4.65999984730.6%
 
4.86000013430.6%
 
4.84000015330.6%
 
10.1899995830.6%
 
7.42000007630.6%
 
4.88000011430.6%
 
3.88000011430.6%
 
4.61000013420.4%
 
3.69000005720.4%
 
4.73999977120.4%
 
4.69999980920.4%
 
11.1499996220.4%
 
7.36000013420.4%
 
4.98999977120.4%
 
6.53999996220.4%
 
9.07999992420.4%
 
7.84999990520.4%
 
14.1300001120.4%
 
8.40999984720.4%
 
8.22999954220.4%
 
12.3400001520.4%
 
3.46000003820.4%
 
4.34000015320.4%
 
Other values (384)44888.2%
 
ValueCountFrequency (%) 
2.51999998110.2%
 
2.79999995210.2%
 
2.83999991410.2%
 
2.90000009510.2%
 
2.91000008610.2%
 
3.01999998110.2%
 
3.03999996210.2%
 
3.07999992410.2%
 
3.17000007610.2%
 
3.2599999910.2%
 
ValueCountFrequency (%) 
22.3899993910.2%
 
22.1900005310.2%
 
20.5499992410.2%
 
20.2900009210.2%
 
20.0499992410.2%
 
19.9599990810.2%
 
19.7600002310.2%
 
19.7510.2%
 
19.3999996210.2%
 
19.3600006110.2%
 

Sulfur_Dioxideglm.LAshumway
Real number (ℝ≥0)

Distinct count282
Unique (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8444291371998824
Minimum0.8600000143051147
Maximum6.570000171661378
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:52.404328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.8600000143
5-th percentile1.430499989
Q12.049999952
median2.74000001
Q33.465000033
95-th percentile4.82650001
Maximum6.570000172
Range5.710000157
Interquartile range (IQR)1.415000081

Descriptive statistics

Standard deviation1.050619745
Coefficient of variation (CV)0.3693604919
Kurtosis0.5755264399
Mean2.844429137
Median Absolute Deviation (MAD)0.7150000334
Skewness0.7892433822
Sum1444.970002
Variance1.103801849
2020-08-25T00:58:52.507382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.43000006781.6%
 
2.7599999971.4%
 
3.13000011461.2%
 
2.77999997151.0%
 
2.2400000151.0%
 
2.52999997151.0%
 
1.85000002451.0%
 
1.86000001451.0%
 
1.78999996251.0%
 
251.0%
 
2.34999990540.8%
 
3.64000010540.8%
 
2.7540.8%
 
2.39000010540.8%
 
3.04999995240.8%
 
2.2599999940.8%
 
2.4900000140.8%
 
1.7400000130.6%
 
1.530.6%
 
3.09999990530.6%
 
3.73000001930.6%
 
2.27999997130.6%
 
1.41999995730.6%
 
3.65000009530.6%
 
2.80999994330.6%
 
Other values (257)40078.7%
 
ValueCountFrequency (%) 
0.860000014310.2%
 
1.07000005210.2%
 
1.10000002410.2%
 
1.12000000530.6%
 
1.15999996710.2%
 
1.16999995710.2%
 
1.19000005710.2%
 
1.20000004810.2%
 
1.2400000110.2%
 
1.2510.2%
 
ValueCountFrequency (%) 
6.57000017210.2%
 
6.5300002110.2%
 
6.48999977110.2%
 
6.05999994310.2%
 
6.03999996210.2%
 
5.92000007610.2%
 
5.90999984720.4%
 
5.65000009510.2%
 
5.63999986610.2%
 
5.48999977110.2%
 

Nitrogen_Dioxide
Real number (ℝ≥0)

Distinct count430
Unique (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.221240176929264
Minimum4.139999866485597
Maximum25.18000030517578
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:52.614094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4.139999866
5-th percentile5.823500085
Q18.25750041
median10.55499983
Q313.51000023
95-th percentile19.24550056
Maximum25.18000031
Range21.04000044
Interquartile range (IQR)5.252499819

Descriptive statistics

Standard deviation4.080036655
Coefficient of variation (CV)0.3635994409
Kurtosis0.5264868262
Mean11.22124018
Median Absolute Deviation (MAD)2.645000219
Skewness0.8661272239
Sum5700.39001
Variance16.6466991
2020-08-25T00:58:52.705110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9.81999969530.6%
 
1030.6%
 
11.1800003130.6%
 
7.86999988630.6%
 
8.64000034330.6%
 
7.69999980930.6%
 
10.0500001930.6%
 
8.60000038130.6%
 
11.3500003830.6%
 
8.73999977130.6%
 
9.69999980930.6%
 
10.520.4%
 
9.85999965720.4%
 
10.8000001920.4%
 
9.92000007620.4%
 
15.9099998520.4%
 
6.32000017220.4%
 
7.88999986620.4%
 
10.6999998120.4%
 
11.0500001920.4%
 
10.2700004620.4%
 
9.94999980920.4%
 
10.4300003120.4%
 
13.5100002320.4%
 
10.8299999220.4%
 
Other values (405)44788.0%
 
ValueCountFrequency (%) 
4.13999986610.2%
 
4.55000019110.2%
 
4.55999994310.2%
 
4.61999988610.2%
 
4.69999980910.2%
 
4.7800002110.2%
 
4.98000001910.2%
 
4.98999977110.2%
 
5.09000015310.2%
 
5.11000013410.2%
 
ValueCountFrequency (%) 
25.1800003110.2%
 
24.0100002310.2%
 
23.9699993110.2%
 
23.5599994710.2%
 
23.2000007610.2%
 
23.1000003810.2%
 
22.7999992410.2%
 
22.7900009210.2%
 
22.2000007610.2%
 
21.8999996210.2%
 

Hydrocarbons
Real number (ℝ≥0)

Distinct count479
Unique (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.484153556072805
Minimum21.56999969482422
Maximum100.12000274658205
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:52.809004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum21.56999969
5-th percentile32.1605011
Q140.2074995
median48.2349987
Q359.69250107
95-th percentile75.13750191
Maximum100.1200027
Range78.55000305
Interquartile range (IQR)19.48500156

Descriptive statistics

Standard deviation13.53358953
Coefficient of variation (CV)0.2680759917
Kurtosis0.01261561357
Mean50.48415356
Median Absolute Deviation (MAD)8.890001297
Skewness0.668064363
Sum25645.95001
Variance183.1580456
2020-08-25T00:58:52.912169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
59.8800010730.6%
 
48.8600006120.4%
 
43.9000015320.4%
 
60.1300010720.4%
 
38.2900009220.4%
 
43.5400009220.4%
 
45.0999984720.4%
 
65.5599975620.4%
 
36.5200004620.4%
 
54.3600006120.4%
 
41.7200012220.4%
 
69.8899993920.4%
 
36.2999992420.4%
 
41.2999992420.4%
 
50.9500007620.4%
 
64.0599975620.4%
 
38.6300010720.4%
 
46.6500015320.4%
 
63.3300018320.4%
 
43.7799987820.4%
 
35.9900016820.4%
 
53.1399993920.4%
 
42.9900016820.4%
 
45.1199989320.4%
 
58.2700004620.4%
 
Other values (454)45790.0%
 
ValueCountFrequency (%) 
21.5699996910.2%
 
26.3700008410.2%
 
26.5400009210.2%
 
27.6399993910.2%
 
27.9500007610.2%
 
28.2000007610.2%
 
28.3400001510.2%
 
28.4799995410.2%
 
28.6900005310.2%
 
29.4300003110.2%
 
ValueCountFrequency (%) 
100.120002710.2%
 
91.4700012210.2%
 
88.8000030510.2%
 
86.9899978610.2%
 
86.8700027510.2%
 
85.3000030510.2%
 
85.1399993910.2%
 
84.1100006110.2%
 
83.3799972510.2%
 
83.0800018310.2%
 

Ozone
Real number (ℝ≥0)

Distinct count432
Unique (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.334448831757223
Minimum1.1399999856948853
Maximum22.049999237060547
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:53.029161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.139999986
5-th percentile2.467000031
Q14.587500095
median7.850000143
Q311.25500011
95-th percentile16.17600021
Maximum22.04999924
Range20.90999925
Interquartile range (IQR)6.667500019

Descriptive statistics

Standard deviation4.445372305
Coefficient of variation (CV)0.5333732794
Kurtosis-0.4163811876
Mean8.334448832
Median Absolute Deviation (MAD)3.3349998
Skewness0.5601385292
Sum4233.900007
Variance19.76133493
2020-08-25T00:58:53.122064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.11999988630.6%
 
5.71000003830.6%
 
4.63999986630.6%
 
3.84999990530.6%
 
9.76000022930.6%
 
3.06999993330.6%
 
9.98999977130.6%
 
2.11999988630.6%
 
10.6700000820.4%
 
7.36000013420.4%
 
10.9499998120.4%
 
4.15000009520.4%
 
6.98999977120.4%
 
14.2799997320.4%
 
4.98000001920.4%
 
8.72000026720.4%
 
6.90999984720.4%
 
5.15000009520.4%
 
2.79999995220.4%
 
8.81999969520.4%
 
9.4399995820.4%
 
9.61999988620.4%
 
4.2800002120.4%
 
4.80000019120.4%
 
10.6000003820.4%
 
Other values (407)45088.6%
 
ValueCountFrequency (%) 
1.13999998610.2%
 
1.46000003810.2%
 
1.59000003310.2%
 
1.85000002410.2%
 
1.87999999510.2%
 
1.88999998610.2%
 
1.91999995710.2%
 
1.98000001910.2%
 
210.2%
 
2.0099999910.2%
 
ValueCountFrequency (%) 
22.0499992410.2%
 
20.2510.2%
 
20.1800003110.2%
 
20.0400009210.2%
 
19.9400005310.2%
 
19.8600006110.2%
 
19.5900001510.2%
 
19.3700008410.2%
 
19.1200008410.2%
 
18.8799991610.2%
 

Particulates
Real number (ℝ≥0)

Distinct count494
Unique (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.4132085559875
Minimum20.25
Maximum97.94000244140624
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:53.219318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum20.25
5-th percentile27.01899977
Q135.85000038
median44.25
Q357.54249954
95-th percentile74.43199883
Maximum97.94000244
Range77.69000244
Interquartile range (IQR)21.69249916

Descriptive statistics

Standard deviation15.13825563
Coefficient of variation (CV)0.3192835095
Kurtosis-0.4569357275
Mean47.41320856
Median Absolute Deviation (MAD)10.72000122
Skewness0.5729688176
Sum24085.90995
Variance229.1667834
2020-08-25T00:58:53.323803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
32.2599983230.6%
 
39.4700012220.4%
 
45.5400009220.4%
 
38.5099983220.4%
 
37.2599983220.4%
 
33.9599990820.4%
 
39.1599998520.4%
 
70.1299972520.4%
 
57.5299987820.4%
 
42.6199989320.4%
 
32.6899986320.4%
 
48.6899986320.4%
 
54.2000007620.4%
 
28.9400005310.2%
 
53.7799987810.2%
 
42.0400009210.2%
 
34.0299987810.2%
 
41.4599990810.2%
 
75.0400009210.2%
 
27.2600002310.2%
 
49.9000015310.2%
 
37.0699996910.2%
 
36.5099983210.2%
 
69.0100021410.2%
 
40.6100006110.2%
 
Other values (469)46992.3%
 
ValueCountFrequency (%) 
20.2510.2%
 
21.7999992410.2%
 
22.7510.2%
 
23.4899997710.2%
 
23.6800003110.2%
 
24.2399997710.2%
 
24.2900009210.2%
 
24.4699993110.2%
 
24.5499992410.2%
 
24.6000003810.2%
 
ValueCountFrequency (%) 
97.9400024410.2%
 
87.0199966410.2%
 
86.5400009210.2%
 
84.9599990810.2%
 
84.6299972510.2%
 
84.2300033610.2%
 
83.3099975610.2%
 
82.9199981710.2%
 
82.8499984710.2%
 
81.7600021410.2%
 

target
Real number (ℝ≥0)

Distinct count365
Unique (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.386515750659733
Minimum4.150000095367432
Maximum30.43000030517578
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-08-25T00:58:53.441700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4.150000095
5-th percentile5.460000038
Q16.667500019
median7.760000229
Q39.182500124
95-th percentile13.7005002
Maximum30.43000031
Range26.28000021
Interquartile range (IQR)2.515000105

Descriptive statistics

Standard deviation2.866401634
Coefficient of variation (CV)0.3417869493
Kurtosis12.72395634
Mean8.386515751
Median Absolute Deviation (MAD)1.255000114
Skewness2.748804864
Sum4260.350001
Variance8.216258328
2020-08-25T00:58:53.537431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
6.55999994351.0%
 
6.38999986640.8%
 
6.19000005740.8%
 
7.88999986640.8%
 
6.94000005740.8%
 
7.80000019140.8%
 
9.07999992440.8%
 
5.23000001930.6%
 
8.60000038130.6%
 
5.15000009530.6%
 
8.85000038130.6%
 
7.42000007630.6%
 
9.02999973330.6%
 
6.5300002130.6%
 
6.65000009530.6%
 
9.18000030530.6%
 
7.40999984730.6%
 
8.02999973330.6%
 
8.23999977130.6%
 
7.38000011430.6%
 
9.36999988630.6%
 
6.7199997930.6%
 
6.51000022930.6%
 
7.55999994330.6%
 
9.19999980920.4%
 
Other values (340)42683.9%
 
ValueCountFrequency (%) 
4.15000009510.2%
 
4.23999977110.2%
 
4.44000005710.2%
 
4.53999996210.2%
 
4.55999994310.2%
 
4.57000017210.2%
 
4.80000019110.2%
 
4.82000017210.2%
 
4.88999986610.2%
 
5.15000009530.6%
 
ValueCountFrequency (%) 
30.4300003110.2%
 
26.2399997710.2%
 
22.9899997710.2%
 
22.9500007610.2%
 
19.8600006110.2%
 
18.9200000810.2%
 
17.9200000810.2%
 
17.7800006910.2%
 
17.0799999210.2%
 
16.6100006110.2%
 

Interactions

2020-08-25T00:58:33.184809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:33.314119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:33.443350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:33.566256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:33.705056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:33.845173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:33.972257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:34.092799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:34.230079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:34.344143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:34.477706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:34.599676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:34.902747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:35.046674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:35.181305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:35.324150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:35.470256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:35.605387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:35.737864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:35.878055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:36.003124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:36.146275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:36.288877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:36.416024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:36.549526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:36.675356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:36.817360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:36.961732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:37.097606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:37.220448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:37.360821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:37.481700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:37.623941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:37.752332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:37.906455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:38.079303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:38.222842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:38.375453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:38.541306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:38.687555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:38.839835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:38.990672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:39.288562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:39.440972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:39.577646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:39.718390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:39.870913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:40.013572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:40.166224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:40.334194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:40.482333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:40.622410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:40.777924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:40.918311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:41.073683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:41.221650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:41.349187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:41.484331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:41.611251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:41.745415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:41.893439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:42.026013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:42.151172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:42.291027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:42.411807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:42.549325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:42.678681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:42.804448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:42.940129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:43.061729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:43.196438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:43.504488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:43.633642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:43.755017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:43.895370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:44.011592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:44.151114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:44.275058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:44.419291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:44.573192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:44.714750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:44.870717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:45.023642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:45.167038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:45.303449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:45.460388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:45.597798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:45.750753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:45.890568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:46.016324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:46.137550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:46.258983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:46.387474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:46.521414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:46.640156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:46.757932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:46.889855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:47.001733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:47.133169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:47.250067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:47.392426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:47.713297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:47.857155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:48.010175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:48.167958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:48.324461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:48.480696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:48.636787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:48.781305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:48.936132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:49.085304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:49.214903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:49.351361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:49.490453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:49.634454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:49.786631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:49.922630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:50.053144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:50.205570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:50.325641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:50.466504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:58:53.666061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:58:53.909013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:58:54.160324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:58:54.409319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:58:50.704915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:58:50.988319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Total_MortalityCardiovascular_MortalityTemperatureRelative_HumidityCarbon_MonoxideSulfur_Dioxideglm.LAshumwayNitrogen_DioxideHydrocarbonsOzoneParticulatestarget
0183.63000597.84999872.37999729.20000111.513.379.6445.7900016.6972.72000111.90
1191.050003104.63999967.19000267.5100028.922.5910.0543.9000026.8349.59999810.75
2180.08999694.36000162.93999961.4199989.483.297.8032.1800004.9855.6800009.33
3184.66999898.05000372.48999858.99000210.283.0413.3940.4300009.2555.1600009.54
4173.60000695.84999874.25000034.79999910.573.3911.9048.5299999.1566.0199978.27
5183.72999695.98000367.87999766.7799997.992.5710.1148.6100018.8044.0099987.55
6171.78999388.62999774.19999735.16000010.512.359.4938.7599987.4247.8300029.12
7177.94999790.84999874.87999755.8600019.843.3810.7245.18999910.6843.5999987.76
8180.00999592.05999864.16999852.6800006.611.505.7731.6500005.8524.9900007.47
9163.71000788.75000067.08999650.9399997.902.567.4340.2000017.6340.4100007.44

Last rows

Total_MortalityCardiovascular_MortalityTemperatureRelative_HumidityCarbon_MonoxideSulfur_Dioxideglm.LAshumwayNitrogen_DioxideHydrocarbonsOzoneParticulatestarget
498168.08999681.16999890.93000056.7300005.802.5514.34000056.15000222.04999963.5999988.62
499171.02999983.91000493.43000043.0099988.372.8317.78000160.13000119.86000157.5299996.48
500173.38000582.36000190.16000452.1600006.602.0515.10000053.72000116.37999954.97000110.94
501154.41999879.73999880.37999757.3100014.341.579.73000040.0099986.99000047.2200018.25
502149.22000173.45999982.37000358.7700007.752.2814.40000053.77000011.89000069.1399997.88
503160.55999879.02999975.34999870.0299994.741.729.10000041.2200017.51000042.1699989.88
504152.42999376.55999872.29000166.8899995.761.499.27000043.7799995.38000045.5900007.96
505165.13999978.51999775.68000056.1500028.451.8914.99000054.6100019.02000070.7200018.69
506168.42999389.43000073.33000245.7300008.241.6311.66000052.9300005.62000057.5800028.79
507171.33999685.48999870.51999760.6699988.011.5811.95000054.3600015.76000062.61000110.11